Manager-Data Science
About The Position
The Global Servicing Decision Science (GSDS) team is the Decision Science engine for Global Servicing, bringing together advanced analytics, data science, AI/GenAI, and decisioning to transform servicing experiences, empower colleagues, and enable intelligent operations. GSDS serves as a centralized Decision Science Center of Excellence, partnering across Product/Capabilities, Control Management, Strategy, Operations, Technology, and enterprise analytics and data science teams. GSDS builds and scales servicing intelligence across Global Servicing—from predictive and proactive servicing, personalization, next-best-action and journey orchestration to conversational AI, agent assist, AI-powered coaching, conduct and quality monitoring, and complaint and dispute intelligence. Our mandate is to build durable, production-ready capabilities that continuously improve decisions through prediction, recommendation, optimization, experimentation, and learning. As a Manager, you will contribute to and increasingly own end-to-end Decision Science work that helps build the next generation of servicing intelligence at American Express. You will translate customer, colleague, and operational challenges into analytical approaches, models, experiments, and scalable AI-driven solutions, working with business and Technology partners to move capabilities from concept through implementation and optimization. This role combines hands-on technical depth with strong business problem solving. You will work on modern capabilities—including Agentic AI, transformer-based recommendation systems, and Conversational AI—while building your ability to lead workstreams, influence partners, and connect technical solutions to measurable customer and business outcomes.
Requirements
- Bachelor’s degree in quantitative fields (e.g., Engineering, Computer Science, Mathematics, Statistics, Economics, Finance).
- Strong analytical and conceptual problem-solving skills, with the ability to structure and solve business problems and work through ambiguity.
- Strong hands-on foundation in data science and machine learning, with proficiency in Python, SQL, or similar tools and experience building and evaluating predictive or decisioning models.
- Experience applying AI/GenAI techniques, with a strong technical foundation in solution development, experimentation, and model/AI evaluation.
- Experience owning analytical workstreams and collaborating across cross-functional teams to move solutions from analysis and development toward implementation and measurable impact.
- Strong written and verbal communication skills, with the ability to translate technical work into clear business recommendations and collaborate effectively with stakeholders.
Nice To Haves
- Experience with Agentic AI/agentic workflows, transformer architectures or transformer-based recommendation systems, Conversational AI, LLMs, or related modern AI capabilities.
- Experience with personalization, recommendation systems, next-best-action, optimization, customer decisioning, or intelligent automation.
- Understanding of the end-to-end Decision Science lifecycle, including experimentation, productionization, monitoring, governance, and continuous optimization.
- Experience working with large-scale customer, behavioral, interaction, or operational datasets and developing reusable data science products.
- Developing commercial acumen and storytelling skills, with the ability to connect analytical and technical choices to customer and business value.
Responsibilities
- Own defined Decision Science problems and workstreams—from problem framing and analysis through solution development, experimentation, measurement, and ongoing optimization—with guidance only on the most complex or ambiguous challenges.
- Develop analytical approaches and decisioning solutions across servicing experiences, personalization and membership value, colleague enablement, and operational excellence.
- Design and develop AI/ML and GenAI solutions spanning prediction, recommendation, optimization, experimentation, and intelligent orchestration.
- Apply and evaluate modern AI approaches—including Agentic AI and agentic workflows, transformer-based recommenders and representation learning, Conversational AI/LLM systems, and advanced personalization or next-best-action methods—to servicing use cases.
- Make sound modeling and evaluation choices, identify data requirements and solution trade-offs, and seek technical guidance where needed to ensure analytical rigor and practical scalability.
- Build and enhance reusable data science products and decisioning capabilities, working with Technology and platform teams to support productionization and measure impact.
- Apply robust experimentation, monitoring, and continuous-learning approaches that improve model/AI quality and business outcomes over time.
- Lead defined workstreams and contribute to the development of other colleagues through collaboration, knowledge sharing, and technical support, while demonstrating strong ownership and problem solving.
- Synthesize analysis into clear recommendations and narratives; communicate effectively with partners and contribute to decisions on solution choices, priorities, and execution.
Benefits
- Competitive base salaries
- Bonus incentives
- 6% Company Match on retirement savings plan
- Free financial coaching and financial well-being support
- Comprehensive medical, dental, vision, life insurance, and disability benefits
- Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
- 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
- Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
- Free and confidential counseling support through our Healthy Minds program
- Career development and training opportunities
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Manager
